Possibility of reinforcement learning using event-related potential toward an adaptive BCI

We applied event-related potential (ERP) to reinforcement signals that are equivalent to reward and punishment signals. We conducted an experiment using an electroencephalogram (EEG) in which volunteers identified the success or failure of an inverted pendulum task. We confirmed that there were differences in the EEG signal depending on whether the task was successful or not and that ERP might be used as a punishment of reinforcement learning. We used a support vector machine (SVM) for recognizing the ERP. We selected the feature vector in SVM that was composed of averages of each 35 msec for each of three channels (F3,Fz,F4) on the frontal area, for a total of 700 msec. Our experimental results suggest that reinforcement learning using ERP can be performed accurately. Finally, we suggest the possibility of developing an adaptive brain-computer interface (BCI) by ERP.